Medical image diagnostic device, medical image diagnostic method, and storage medium

ABSTRACT

A medical image diagnostic apparatus of embodiments includes an acquisition unit and a processing unit. The acquisition unit acquires physical characteristics data of an examination subject and information about an imaging target portion. The processing unit is configured to output bed position information about the examination subject according to the acquired physical characteristics data and the information about the imaging target portion by inputting the physical characteristics data and the information about the imaging target portion acquired by the acquisition unit to a trained model which is configured to output bed position information on the basis of physical characteristics data and information about a imaging target portion.

CROSS-REFERENCE TO RELATED APPLICATION

Priority is claimed on Japanese Patent Application No. 2019-188878,filed Oct. 15, 2019, and Japanese Patent Application No. 2018-200310,filed Oct. 24, 2018, the content of which is incorporated herein byreference.

BACKGROUND Field

Embodiments of the present invention relate to a medical imagediagnostic apparatus, a medical image diagnosis method, and a storagemedium.

Background

Conventionally, in a medical image diagnostic apparatus such as an X-rayCT apparatus, a method for changing the height of a bed on the basis ofa calculation result when calculating a height that enables anexamination subject to easily get on and off according to the height ofa subject has been disclosed.

In addition, in conventional medical imaging diagnostic apparatuses, afield of view (FOV) is set through manual operation in many cases andsome time may be required for the operations thereof before imaging.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a configuration diagram of an X-ray CT apparatus according toa first embodiment.

FIG. 2 is a diagram showing an example of data stored in a memory.

FIG. 3 is a configuration diagram of a scan control function.

FIG. 4 is a diagram showing an example of physical characteristics dataand information about an imaging target portion stored in examinationsubject information.

FIG. 5 is a diagram showing output processing performed by a trainedmodel.

FIG. 6 is a diagram showing processing of a learning function.

FIG. 7 is a diagram showing a look-up table of choices of physicalcharacteristics data.

FIG. 8 is a diagram showing an environment in which an X-ray CTapparatus is used.

FIG. 9 is a diagram showing an environment in which an X-ray CTapparatus is used.

FIG. 10 is a flowchart showing an example of a flow of imagingprocessing performed by an X-ray CT apparatus.

FIG. 11 is a flowchart showing an example of a flow of learningprocessing performed by a learning function.

FIG. 12 is a diagram showing a problem occurring in an image diagnosticapparatus of a reference example.

FIG. 13 is a configuration diagram of a nuclear medical diagnosticapparatus according to a second embodiment.

FIG. 14 is a diagram showing an example of data stored in a memory.

FIG. 15 is a diagram showing output processing performed by a trainedmodel.

FIG. 16 is a diagram showing a trained model generation processing of alearning function.

FIG. 17 is a flowchart showing an example of a flow of imagingprocessing performed by a nuclear medical diagnostic apparatus.

FIG. 18 is a configuration diagram of an X-ray CT apparatus according toa third embodiment.

FIG. 19 is a diagram showing an example of data stored in a memory.

FIG. 20 is a configuration diagram of a scan control function.

FIG. 21 is a diagram showing an example of recommended position settingconditions.

FIG. 22 is a diagram showing processing of a processing function.

FIG. 23 is a flowchart showing an example of a flow of imagingprocessing performed by an X-ray CT apparatus.

FIG. 24 is a flowchart showing an example of a flow of recommendedposition derivation processing performed by an automatic alignmentfunction.

DETAILED DESCRIPTION

A medical image diagnostic apparatus of embodiments includes anacquisition unit and a processing unit. The acquisition unit acquiresphysical characteristics data of an examination subject and informationabout an imaging target portion. The processing unit is configured tooutput bed position information with respect to the examination subjectaccording to information about the acquired physical characteristicsdata and imaging target portion by inputting the physicalcharacteristics data and the information about the imaging targetportion acquired by the acquisition unit to a trained model which isconfigured to output bed position information on the basis of physicalcharacteristics data and information about an imaging target portion.

Hereinafter, a medical image diagnostic apparatus, a medical imagediagnosis method, and a storage medium of embodiments will be describedwith reference to the drawings. The medical image diagnostic apparatusis, for example, an apparatus that allows diagnosis to be performed onan examination subject by performing processing on medical images, suchas an X-ray computed tomography (CT) apparatus, a positron emissiontomography (PEI) apparatus, a single photon emission computed tomography(SPECT) inspection apparatus or the like. Although description will begiven in which the medical image diagnostic apparatus is an X-ray CTapparatus in a first embodiment and a third embodiment and a nuclearmedical diagnostic apparatus such as a SPECT inspection apparatus in asecond embodiment, the present invention is not limited thereto.

First Embodiment

FIG. 1 is a configuration diagram of an X-ray CT apparatus 1 accordingto a first embodiment. The X-ray CT apparatus 1 is an example of amedical image diagnostic apparatus. The X-ray CT apparatus 1 includes,for example, a holding device 10, a bed device 30, and a console device40. Although FIG. 1 shows both a diagram of the holding device 10 viewedin a Z-axis direction and a diagram viewed in an X-axis direction forconvenience of description, there is actually one holding device 10. Inembodiments, a rotation axis of a rotary frame 17 in a non-tilted stateor a longitudinal direction of a top board 33 of the bed device 30 isdefined as a Z-axis direction, an axis at a right angle to the Z-axisdirection that is parallel to the floor is defined as an X-axisdirection, and a direction at a right angle to the Z-axis direction thatis perpendicular to the floor is defined as a Y-axis direction.

The holding device 10 includes, for example, an X-ray tube 11, a wedge12, a collimator 13, an X-ray high voltage device 14, an X-ray detector15, a data collection system (hereinafter, data acquisition system (DAS)16, the rotary frame 17 and a control device 18.

The X-ray tube 11 generates X rays by radiating thermions from a cathode(filament) to an anode (target) according to application of a highvoltage from the X-ray high voltage device 14. The X-ray tube 11includes a vacuum tube. For example, the X-ray tube 11 may be a rotatinganode type X-ray tube which generates X rays by radiating thermions to arotating anode.

The wedge 12 is a filter for controlling the amount of X rays radiatedfrom the X-ray tube 11 to an examination subject P. The wedge 12attenuates X rays transmitted through the wedge 12 such that adistribution of the amount of X rays radiated from the X-ray tube 11 tothe examination subject P becomes a predetermined distribution. Thewedge 12 is also called a wedge filter or a bow-tie filter. For example,the wedge 12 may be manufactured by processing aluminum such that it hasa predetermined target angle and a predetermined thickness.

The collimator 13 is a mechanism for narrowing a radiation range of Xrays that have been transmitted through the wedge 12. The collimator 13narrows a radiation range of X rays, for example, by forming a slitaccording to combination of a plurality of lead plates. The collimator13 may also be called an X-ray aperture.

The X-ray high voltage device 14 includes, for example, a high voltagegeneration device and an X-ray control device. The high voltagegeneration device has an electrical circuit including a transformer(trans), a rectifier and the like and generates a high voltage to beapplied to the X-ray tube 11. The X-ray control device controls anoutput voltage of the high voltage generation device in response to theamount of X rays generated by the X-ray tube 11. The high voltagegeneration device may perform voltage boosting through theaforementioned transformer or perform voltage boosting through aninverter. The X-ray high voltage device 14 may be provided in the rotaryframe 17 or provided on the side of a fixed frame (not shown) of theholding device 10.

The X-ray detector 15 detects the intensity of X rays that have beengenerated by the X-ray tube 11, passed through the examination subject Pand applied to the X-ray detector 15. The X-ray detector 15 outputs anelectrical signal (an optical signal or the like is possible) inresponse to the detected intensity of X rays to the DAS 18. The X-raydetector 15 includes, for example, a plurality of X-ray detectionelement strings. The plurality of X-ray detection element strings areobtained by arranging a plurality of X-ray detection elements in achannel direction along an arc having the focus of the X-ray tube 11 asa center. The plurality of X-ray detection element strings are arrangedin a slice direction (row direction).

The X-ray detector 15 is, for example, an indirect detector including agrid, a scintillator array and an optical sensor array. The scintillatorarray includes a plurality of scintillators. Each scintillator hasscintillator crystals. Scintillator crystals emit an amount of light inresponse to the intensity of input X rays. The grid is disposed on asurface of the scintillator array to which X rays are input and includesan X-ray shielding plate having a function of absorbing scattered Xrays. Meanwhile, there is a case in which the grid is called acollimator (one-dimensional collimator or two-dimensional collimator).The optical sensor array includes, for example, optical sensors such asphotomultipliers (PMTs). The optical sensor array outputs an electricalsignal in response to the amount of light emitted from thescintillators. The X-ray detector 15 may be a direct conversion typedetector including a semiconductor element which converts input X raysinto an electrical signal.

The DAS 16 includes, for example, an amplifier, an integrator, and anA/D converter. The amplifier performs amplification processing on anelectrical signal output from each X-ray detection element of the X-raydetector 15. The integrator integrates amplified electrical signals overa view period (which will be described later). The A/D converterconverts an electrical signal representing an integration result into adigital signal. The DAS 16 outputs detected data based on the digitalsignal to the console device 40. The detected data is a digital value ofan X-ray intensity identified through a channel number and a stringnumber of an X-ray detection element that is a generation source, and aview number indicating a collected view. A view number is a number thatvaries according to rotation of the rotary frame 17 and is, for example,a number that increases according to rotation of the rotary frame 17.Accordingly, a view number is information representing a rotation angleof the X-ray tube 11. A view period is a period from a rotation anglecorresponding to a certain view number to a rotation angle correspondingto the next view number. The DAS 16 may detect view switching through atiming signal input from the control device 18, an internal timer or asignal acquired from a sensor which is not shown. When X rays arecontinuously emitted by the X-ray tube 11 during full scanning, the DAS16 collects detected data groups corresponding to the entirecircumference (360 degrees). When X rays are continuously emitted by theX-ray tube 11 during half scanning, the DAS 16 collects detected datacorresponding to half a circumference (180 degrees).

The rotary frame 17 is an annular member which supports the X-ray tube11, the wedge 12, the collimator 13 and the X-ray detector 15 such thatthe X-ray tube 11, the wedge 12 and the collimator 13 face the X-raydetector 15. The rotary frame 17 is rotatably supported by a fixed framehaving the examination subject P introduced thereinto as a center. Therotary frame 17 additionally supports the DAS 16. Detected data outputfrom the DAS 16 is transmitted from a transmitter having a lightemitting diode (LED) provided in the rotary frame 17 to a receiverhaving a photodiode provided in a non-rotary part (e.g., a fixed frame)of the holding device 10 through optical communication and forwarded tothe console device 40 through the receiver.

Meanwhile, a method of transmitting detected data from the rotary frame17 to a non-rotary part is not limited to the aforementioned methodusing optical communication and any non-contact type transmission methodmay be employed. The rotary frame 17 is not limited to an annular memberand may be a member such as an arm as long as it can support and rotatethe X-ray tube 11 and the like.

Although the X-ray CT apparatus 1 may be, for example, aRotate/Rotate-Type X-ray CT apparatus (third-generation CT) in whichboth the X-ray tube 11 and the X-ray detector 15 are supported by therotary frame 17 and rotate around the examination subject P, it is notlimited thereto and may be a Stationary/Rotate-Type X-ray CT apparatus(fourth-generation CT) in which a plurality of X-ray detection elementsarranged in an annular shape are fixed to a fixed frame and the X-raytube 11 rotates around the examination subject P.

The control device 18 includes, for example, a processing circuit havinga processor such as a central processing unit (CPU) and a drivingmechanism including a motor, an actuator and the like. The controldevice 18 receives an input signal from an input interface 43 attachedto the console device 40 or the holding device 10 and controlsoperations of the holding device 10 and the bed device 30.

For example, the control device 18 may rotate the rotary frame 17, tiltthe holding device 10 or move the top board 33 of the bed device 30.When the control device 18 tilts the holding device 10, the controldevice 18 rotates the rotary frame 17 on an axis parallel to the Z-axisdirection on the basis of an inclination angle (tilt angle) input to theinput interface 43. The control device 18 ascertains a rotation angle ofthe rotary frame 17 through an output of a sensor which is not shown,and the like. In addition, the control device 18 provides the rotationangle of the rotary frame 17 to the processing circuit 50 at any time.The control device 18 may be provided in the holding device 10 orprovided in the console device 40.

The bed device 30 mounts and moves the examination subject P to bescanned and introduces him or her into the rotary frame 17 of theholding device 10. The bed device 30 includes, for example, a base 31, abed driving device 32, the top board 33, and a supporting frame 34. Thebase 31 includes a housing which supports the supporting frame 34 suchthat the supporting frame 34 can move in a vertical direction (Y-axisdirection). The bed driving device 32 includes a motor and an actuator.The bed driving device 32 moves the top board 33 on which theexamination subject P is mounted in the longitudinal direction (Z-axisdirection) of the top board 33 along the supporting frame 34. The topboard 33 is a plate-shaped member on which the examination subject Pismounted.

The bed driving device 32 may move the supporting frame 34 in thelongitudinal direction of the top board 33 as well as the top board 33.Further, contrary to the above, the holding device 10 may be movable inthe Z-axis direction and the rotary frame 17 may be controlled such thatit comes near the examination subject P in accordance with movement ofthe holding device 10. In addition, both the holding device 10 and thetop board 33 may be configured such that they are movable.

The console device 40 includes, for example, a memory 41, a display 42,the input interface 43, a network connection circuit 44, and aprocessing circuit 50. Although the console device 40 is described as abody separate from the holding device 10 in embodiments, some or allcomponents of the console device 40 may be included in the holdingdevice 10.

The memory 41 is realized, for example, by a semiconductor element suchas a random access memory (RAM) or a flash memory, a hard disk, anoptical disc, or the like. The memory 41 stores, for example, detecteddata, projection data, reconstructed images, CT images, and the like.Such data may be stored in an external memory with which the X-ray CTapparatus 1 can communicate instead of the memory 41 (or in addition tothe memory 41). For example, the external memory may be controlledthrough a cloud server which manages the external memory by receiving aread request.

The display 42 displays various types of information. For example, thedisplay 42 displays medical images (CT images) generated by a processingcircuit, a graphical user interface (GUI) image through which variousoperations from an operator are received, and the like. For example, thedisplay 42 may be a liquid crystal display, a cathode ray tube (CRT), anorganic electroluminescence (EL) display, or the like. The display 42may be provided in the holding device 10. The display 42 may be adesktop type or a display device (e.g., a tablet terminal) which canwirelessly communicate with the main body of the console device 40.

The input interface 43 receives various input operations from anoperator and outputs electrical signals representing details of receivedinput operations to the processing circuit 50. For example, the inputinterface 43 may receive operations of inputting collection conditionswhen detected data or projection data (which will be described later) iscollected, reconstruction conditions when a CT image is reconstructed,image processing conditions when a postprocessing image is generatedfrom a CT image, and the like. For example, the input interface 43 maybe realized by a mouse, a keyboard, a touch panel, a trackball, aswitch, a button, a joystick, a foot pedal, a camera, an infraredsensor, a microphone, or the like. The input interface 43 may beprovided in the holding device 10. In addition, the input interface 43may be realized by a display device (e.g., a tablet terminal) which canwirelessly communicate with the main body of the console device 40.

The network connection circuit 44 includes, for example, a network cardhaving a printed circuit board, a wireless communication module, or thelike. The network connection circuit/Id implements an informationcommunication protocol in accordance with a network form of a connectiontarget. Networks include, for example, a local area network (LAN), awide area network (WAN), the Internet, a cellular network, a dedicatedline, and the like.

The processing circuit 50 controls the overall operation of the X-ray CTapparatus 1. The processing circuit 50 executes, for example, a systemcontrol function 51, a preprocessing function 52, a reconstructionprocessing function 53, an image processing function 54, a scan controlfunction 55, a display control function 56, a learning function 57, andthe like. For example, the processing circuit 50 may realize thesefunctions by a hardware processor executing a program stored in thememory 41.

For example, the hardware processor may refer to a circuitry such as aCPU, a graphics processing unit (GPU), an application specificintegrated circuit (ASIC), a programmable logic device (e.g., a simpleprogrammable logic device (SPLD) or a complex programmable logic device(CPLD)) or a field programmable gate array (FPGA). A program may bedirectly incorporated into a hardware processor circuit instead of beingstored in the memory 41. In this case, a hardware processor realizesfunctions by reading and executing a program incorporated into thecircuit. A hardware processor is not limited to a single circuit as aconfiguration, and a plurality of independent circuits may be combinedto constitute a single hardware processor to realize respectivefunctions. In addition, respective functions may be realized byintegrating a plurality of components into a single hardware processor.

Components included in the console device 40 or the processing circuit50 may be distributed and realized by a plurality of hardware circuits.The processing circuit 50 may be realized by a processing device whichcan communicate with the console device 40 instead of being included inthe console device 40. For example, the processing device may be aworkstation connected to a single X-ray CT apparatus or a device (e.g.,a cloud server) which is connected to a plurality of X-ray CTapparatuses and integrally executes processes equivalent to those of theprocessing circuit 50 which will be described below.

The system control function 51 controls various functions of theprocessing circuit 50 on the basis of input operations received by theinput interface 43.

The processing function 52 performs preprocessing such as logarithmicconversion processing, offset correction processing, inter-channelsensitivity correction processing and beam hardening correction ondetected data output from the DAS 16, generates projection data, andstores the generated projection data in the memory 41.

The reconstruction processing function 53 performs reconstructionprocessing through a filter correction reverse projection method, asequential approximation reconstruction method or the like on projectiondata generated by the preprocessing function 52, generates a CT imageand stores the generated CT image in the memory 41.

The image processing function 54 converts a CT image into athree-dimensional image or section image data with an arbitrary sectionthrough a known method on the basis of an input operation received bythe input interface 43. Conversion into a three-dimensional image may beperformed by the preprocessing function 52.

The scan control function 55 instructs the X-ray high voltage device 14,the DAS 16, the control device 18 and the bed driving device 32 tocontrol detected data collection processing in the holding device 10.The scan control function 55 controls operation of each component whenimaging for collecting scan images and imaging of images used fordiagnosis are performed.

The display control function 56 controls a display mode of the display42.

The learning function 57 learns position information of the bed device30. The learning function 57 will be described later. The learningfunction 57 is an example of a “learning unit.”

According to the above-described configuration, the X-ray CT apparatus 1scans the examination subject P in a mode such as helical scan,conventional scan or step-and-shot. The helical scan is a mode ofrotating the rotary frame 17 while moving the top board 33 to scan theexamination subject P in a spiral form. The conventional scan is a modeof rotating the rotary frame 17 in a state in which the top board 33 isstopped to scan the examination subject P in a circular orbit. Thestep-and-shot is a mode of moving the position of the top board 33 atspecific intervals to perform the conventional scan in a plurality ofscan areas.

FIG. 2 is a diagram showing an example of data stored in the memory 41.As shown in FIG. 2, for example, information such as examination subjectinformation 41-1, detected data 41-2, projection data 41-3,reconstructed images 41-4, recommended positions 41-5, trained models41-6 and the like generated by the processing circuit 50 may be storedin the memory 41.

FIG. 3 is a configuration diagram of the scan control function 55. Thescan control function 55 includes, for example, an examination subjectinformation acquisition function 55-1, an automatic alignment function55-2, a manual alignment function 55-3 and a scan execution function55-4.

The examination subject information acquisition function 55-1 acquiresphysical characteristics data and information about an imaging targetportion associated with the examination subject P through theexamination subject information 41-1 and outputs the acquired physicalcharacteristics data and information about the imaging target portion tothe automatic alignment function 55-2. The examination subjectinformation acquisition function 55-1 is an example of the “acquisitionunit.” Further, the examination subject information acquisition function55-1 may receive input of physical characteristics data and informationabout an imaging target portion input by an operator through the inputinterface 43 and acquire details of the input.

FIG. 4 is a diagram showing an example of physical characteristics dataand information about an imaging target portion stored in theexamination subject information 41-1. The examination subjectinformation 41-1 includes, for example, physical characteristics data ofan examination subject (e.g., information about an ID for identifyingthe examination subject, a sex and an age, and actual measurement valuessuch as a height, a weight, a chest girth and an abdominalcircumference) and information about a imaging target portion (e.g., ahead, a chest or the like). Correct values (actual measurement values)of the examination subject P may be input or estimated values may be setby an operator as the physical characteristics data.

Further, the examination subject information 41-1 may include testpurposes and precaution information of the examination subject P. Theexamination subject information 41-1 includes, for example, testpurposes such as “medical checkup” and “periodic health examination,”the names of diseases in therapy or possible diseases, and precautioninformation such as “there is abdominal inflation,” “there is noabdominal inflation” or the like. When the trained model 41-6 does notconsider a state in which the abdomen of the examination subject P hasinflated as shown in FIG. 12 which will be described later, an outputrecommended position is likely to be separated from a imaging positionsuitable for the examination subject P even when conditions such as theheight and the weight are satisfied. Accordingly, when the trained model41-6 is selected through the automatic alignment function 55-2, it maybe desirable to refer to the test purpose and symptoms of theexamination subject P.

Referring back to FIG. 3, the automatic alignment function 55-2 assistsalignment by setting a recommended position of the top board 33 on thebasis of physical characteristics data and information about an imagingtarget portion output from the examination subject informationacquisition function 55-1. Alignment will be described later. Theautomatic alignment function 55-2 sets a recommended position of the topboard 33 by selecting a trained model 41-6 generated by the learningfunction 57 which will be described later depending on the physicalcharacteristics data such as the height and weight of the examinationsubject P, and the like and inputting the physical characteristics dataof the examination subject P and the information about the imagingtarget portion to the selected trained model 41-6. The automaticalignment function 55-2 selects, for example, a trained model 41-6trained using the physical characteristics data of the examinationsubject P and data close to the imaging target portion. If it does notexist, the manual alignment function 55-3 receives an input of anoperator of the X-ray CT apparatus 1 and performs alignment as will bedescribed later.

The trained model 41-6 is prepared, for example, for each of a pluralityof groups in which labels have been provided to the age, sex, height,weight, an imaging target portion and the like of the examinationsubject P, which will be described later. The automatic alignmentfunction 55-2 selects a trained model 41-6 corresponding to a groupmatching the age, sex, height, weight, an imaging target portion and thelike of the examination subject P.

A recommended position of the top board 33 includes, for example, theheight with respect to a reference position of the top board 33 and apositional relation between the top board 33 and the holding device 10(or a positional relation between the examination subject P and theholding device 10) immediately before imaging of the examination subjectP is started. A recommended position of the top board 33 may include aheight with respect to the reference position of the top board 33 and apositional relation between the top board 33 and the holding device 10at each of timings such as a timing at which the examination subject Pgets on or off the top board 33 or a timing before imaging is started(e.g., timing of imaging preparation such as fixture setting). Thereference position may be a floor on which the bed device 30 isinstalled or a lowest position at which the top board 33 can be placed.The automatic alignment function 55-2 stores a recommended position ofthe top board 33 in the memory 41 as a recommended position 41-5. Theautomatic alignment function 55-2 is an example of the “processingunit.”

It is desirable that the automatic alignment function 55-2 provide sometrigger operations (e.g., a lock release operation performed by anoperator and an audio guidance for indicating movement of the top board33) in order to call attention of the operator and the examinationsubject P on the top board 33 before movement of the top board 33 to therecommended position is started.

Meanwhile, when there is no trained model 41-6 suitable for theexamination subject P, the automatic alignment function 55-2 may give upsetting of a recommended position of the top board 33 and allow themanual alignment function 55-3 to proceed with processing.

FIG. 5 is a diagram showing output processing performed by the trainedmodel 41-6. The automatic alignment function 55-2 outputs a recommendedposition stored in the recommended position 41-5 of the top board 33 byinputting the physical characteristics data of the examination subject Pand information about the imaging target portion to the trained model41-6 as parameters. It is possible to reduce an alignment time requireduntil the examination subject P is imaged (scanned) by performingsubsequent processing on the basis of the recommended position. Theautomatic alignment function 55-2 stores the recommended position 41-5roughly set in this manner in the memory 41.

The manual alignment function 55-3 receives an input of an operator ofthe X-ray CT apparatus 1 with respect to whether the recommendedposition set by the automatic alignment function 55-2 will be used. Theinput received here includes an input for the purpose of realignment andan input for the purpose of fine adjustment. The manual alignmentfunction 55-3 receives an input operation of the operator forrealignment or fine adjustment and controls the operation of the topboard 33. The manual alignment function 55-3 reflects a result ofrealignment or fine adjustment in the recommended position 41-5.

Hereinafter, alignment performed by the automatic alignment function55-2 and the manual alignment function 55-3 will be described. Alignmentis to move the top board 33 to an imaging start position after theexamination subject P has taken an imaging posture on the top board 33.

When manual alignment is performed, the operator of the X-ray CTapparatus 1 moves the top board 33 by operating the input interface 43such as a button or a foot pedal and moves the examination subject P toa imaging start position (in a state in which a portion to be imaged isincluded between the X-ray tube 11 and the DAS 16). The X-ray CTapparatus 1 assists alignment performed by the operator, for example, byradiating an irradiation lamp (laser light) for alignment. For example,the operator of the X-ray CT apparatus 1 may align an FOV center whilechecking a state in which an irradiation lamp for alignment in theZ-axis direction of the holding device 10 has been aligned with thelateral center line of the body of the examination subject P and thenchecking a state in which an irradiation lamp for alignment in theX-axis direction has been aligned with a portion (e.g., the eyes, ears,sternoclavicular joint or the like of the examination subject P) usedfor alignment. The automatic alignment function 55-2 which will bedescribed later can alleviate a labor required for this alignment andefficiently perform operations before imaging.

Referring back to FIG. 3, the scan execution function 55-4 performsimaging at a position of the top board 33 set by the manual alignmentfunction 55-3 to acquire a CT image. The scan execution function 55-4stores the acquired captured image in the form of detected data 41-2,projection data 41-3, a reconstructed image 41-4 or the like.

Hereinafter, processing performed by the learning function 57 will bedescribed. The learning function 57 acquires sets of physicalcharacteristics data of a plurality of examination subjects andinformation about imaging target portions from the memory 41 or anexternal device. The learning function 57 generates a trained model 41-6by performing machine learning using the acquired physicalcharacteristics data of examination subjects and information aboutimaging target portions as learning data and using position informationof the top board 33 set for the same examination subject as teacherdata. The learning function 57 is an example of a “model generationunit.” In addition, the learning function 57 may be realized by anexternal device.

FIG. 6 is a diagram showing processing of the learning function 57. Thelearning function 57 inputs learning data of a plurality of sets to amachine learning model in which connection information and the like havebeen defined in advance and parameters such as a connection coefficienthave been provisionally set and adjusts parameters in the machinelearning model such that the result of input becomes close to teacherdata corresponding to the learning data. For example, the learningfunction 57 may generate a machine learning model for generating atrained model using physical characteristics data of a certainexamination subject and information about a imaging target portionincluded in the examination subject information 41-1 as learning dataand using position information of the top board 33 when the examinationsubject is imaged included in the recommended position 41-5 as teacherdata.

The learning function 57 adjusts parameters of the machine learningmodel, for example, through back propagation (back error propagationmethod). The machine learning model is, for example, a deep neuralnetwork (DNN) using a convolution neural network (CNN). Further, themachine learning model may set a weighting for each piece of learningdata such that newer learning data is more easily reflected in anoutput.

The learning function 57 ends processing when back propagation has beenperformed on a predetermined number of sets of learning data and teacherdata corresponding thereto. A machine learning model at that time isstored as a trained model 41-6. Further, the learning function 57 maygenerate a trained model 41-6 using physical characteristics data of acertain examination subject, information about an imaging targetportion, and position information of the top board 33 during theimaging.

Meanwhile, a rate limiter may be provided in the learning function 57 tolimit a difference between learning data and learning data 1 cyclebefore the learning data to a predetermined value or less.

FIG. 7 is a diagram showing a look-up table of choices of physicalcharacteristics data. It is possible to provide choices set in advanceas shown in FIG. 7 and select physical characteristics data therefrom byan operator instead of inputting estimated values. In the look-up table,for example, choices such as “teenager” and “twenty” may be provided asages and choices such as “tall,” “normal” and “short” may be provided asheights. In addition, in the look-up table, choices indicating anaverage weight (55 to 70 [kg] in FIG. 7), weights equal to or greaterthan the average weight (70 [kg] or more in FIG. 7) and weights equal toor less than the average weight (55 [kg] or less in FIG. 7) may beprovided as weights and choices suggesting weights such as “normal,”“slightly obese” and “obese” may be provided. The X-ray CT apparatus 1allows the operator to select any of choices of the look-up table suchthat the operator can alleviate a labor in measurement and input ofcorrect values.

Meanwhile, choices of the look-up table as shown in FIG. 7 may beassigned to the trained model 41-6 as labels of ages, sexes, heights,weights and the like of physical characteristics data.

Further, the trained model 41-6 used by the learning function 57 orlearning data and teacher data used by the learning function 57 may begenerated by another X-ray CT apparatus 1.

FIG. 8 and FIG. 9 are diagrams describing environments in which theX-ray CT apparatus 1 is used. A trained model generated by another X-rayCT apparatus 1 or learning data and teacher data used by the learningfunction 57 are provided by a service representative MP or the like ofthe manufacturer of the X-ray CT apparatus 1 for a facility H1 using theX-ray CT apparatus 1 as shown in FIG. 8. Furthermore, a trained modelgenerated by another X-ray CT apparatus 1 or learning data and teacherdata used by the learning function 57 may be shared by a plurality offacilities H1 to HN (N is any natural number) using the X-ray CPapparatuses 1 through a cloud server CS or the like via an externalnetwork NW, for example, as shown in FIG. 9. The cloud server CS mayexclusively perform the same learning as the learning function 57. Inaddition, the trained model provided to the respective facilities H1 toHN in FIG. 9 may be provided by the manufacturer of the X-ray CTapparatus 1.

A data structure or a program serving as the trained model 41-6 may bestored as the trained model 41-6 in the memory 41 of the X-ray CTapparatus 1 when the X-ray CT apparatus is sold or installed as thetrained model 41-6 in the memory 41 of the X-ray CT apparatus 1 afterthe X-ray CT apparatus 1 is sold. When a trained model 41-6 in which aimaging result of the X-ray CT apparatus 1 has been reflected has notbeen generated, for example, the learning function 57 may search trainedmodels provided by the manufacturer or trained models provided by otherfacilities using the X-ray CT apparatus and allow the operator todetermine whether to use a trained model that is a search result.

FIG. 10 is a flowchart showing an example of a flow of imagingprocessing performed by the X-ray CT apparatus 1.

First, the examination subject information acquisition function 55-1acquires physical characteristics data of the examination subject P andinformation about an imaging target portion stored in the examinationsubject information 41-1 (step S100). Next, the automatic alignmentfunction 55-2 determines whether there is a trained model 41-6 suitablefor the examination subject P (step S102). When it is determined thatthere is a suitable trained model 41-6, the automatic alignment function55-2 applies the physical characteristics data and the information aboutthe imaging target portion that are input parameters to the trainedmodel (step S104) and receives an input of a result of determination ofan operator with respect to whether to move the top board 33 to arecommended position output as a result of application of the data andinformation to the trained model (step S106). Further, if there is aplurality of trained models 41-6 selected in step S102, presence of aplurality of choices may be displayed on the display 42 such that theoperator selects a trained model in step S104 or a most suitable trainedmodel 41-6 may be automatically selected in step S104.

When it is determined that an input of the operator which indicatesmovement of the top board 33 to the recommended position has beenreceived in step S106, the automatic alignment function 55-2 moves thetop board 33 to the recommended position (step S108). When it isdetermined that an input of the operator which indicates movement of thetop board 33 to the recommended position has not been received in stepS106, that is, when it is determined that an input indicating manualalignment has been received or when it is not determined that there is asuitable trained model 41-6 by the automatic alignment function 55-2 instep S102, the manual alignment function 55-3 receives an input ofalignment by the operator and moves the top board 33 (step S110).Subsequently, the learning function 57 stores an alignment result instep S110 as learning data (step S112).

The manual alignment function 55-3 receives an input operation of finelyadjusting a final position of the top board 33 after step S110 orprocessing of step S108 performed by the automatic alignment function55-2 (step S114). Subsequently, the scan execution function 55-4performs imaging (step S116). Hereby, description of processing of thisflowchart ends. Meanwhile, the above-described step S112 may beperformed after the processing of step S108.

FIG. 11 is a flowchart showing an example of a flow of learningprocessing performed by the learning function 57. The flowchart of FIG.11 may be performed whenever the X-ray CT apparatus 1 ends imaging ofone examination subject or when the amount of learning data stored instep S112 of FIG. 10 is equal to or greater than a predetermined numberof sets as a result of alignment through the aforementioned manualoperation performed by the operator.

First, the learning function 57 acquires learning data of one set (stepS200). Subsequently, the learning function 57 inputs the learning dataof one set acquired in step S200 to a machine learning model (step S202)and back propagates an error from teacher data corresponding to thelearning data of one set (step S204).

Subsequently, the learning function 57 determines processing of stepS202 and processing of step S204 have been performed for learning dataof a predetermined number of sets (step S206). When processing of stepS202 and processing of step S204 have not been performed for learningdata of the predetermined number of sets, the learning function 57returns to processing of step S200. When processing of step S202 andprocessing of step S204 have been performed for learning data of thepredetermined number of sets, the learning function 57 determines atrained model 41-6 using parameters at that time (step S208) and endsprocessing of this flowchart.

Here, an image diagnostic apparatus of a reference example will bedescribed. FIG. 12 is a diagram showing a problem occurring in the imagediagnostic apparatus of the reference example. The image diagnosticapparatus of the reference example does not include a function ofperforming alignment on the basis of physical characteristics data of anexamination subject and information about an imaging target portion asin the X-ray CT apparatus 1 of the embodiment.

For example, when a imaging center (an axis during imaging) SL isautomatically set at an average position with respect to informationsuch as the height and weight of an examination subject P, the sex andthe age of the examination subject P during imaging of a sectional imageof the examination subject P, there is a case in which an FOV centerdeviates due to the body type of the examination subject P and thussetting of the imaging center is inappropriate. When the abdomen of theexamination subject P has inflated due to ascites or the like, as shownin FIG. 12, overflow artifacts are highly likely to be detected atpositions indicated by positions O1 and O2 in a CT image. In view ofthis, the X-ray CT apparatus 1 of the embodiment performs alignment onthe basis of physical characteristics data of an examination subject andinformation about an imaging target portion including an artifactfactor, as described above, and thus can prevent occurrence of such aproblem.

According to the above-described X-ray CT apparatus 1 of the firstembodiment, it is possible to efficiently perform operations such asalignment of an examination subject P before imaging by including theautomatic alignment function 55-2 which outputs a recommended positionof the top board 33 by inputting to the trained model 41-6, theexamination subject information 41-1 including physical characteristicsdata of the examination subject and information about an imaging targetportion acquired by the examination subject information acquisitionfunction 55-1.

Second Embodiment

Hereinafter, a nuclear medical diagnostic apparatus 2 of a secondembodiment will be described. Meanwhile, in the description below,components and functions the same as those in the first embodiment aredenoted by the same reference signs and detailed description thereofwill be omitted. Furthermore, “A” is attached to reference signs ofcomponents or functions different from those in the first embodimentwhile having the same names as those in the first embodiment.

FIG. 13 is a configuration diagram of the nuclear medical diagnosticapparatus 2 according to the second embodiment. The nuclear medicaldiagnostic apparatus 2 includes, for example, a scanner device 60 and aconsole device 40A. In the second embodiment, the console device 40A isan example of a medical image diagnostic apparatus.

[Scanner Device]

The scanner device 60 includes, for example, a fixed holder 62, a rotaryholder 64, a rotation driving device 66, three gamma ray detectors 80and collimators 82 attached to the rotary holder 64 while being shiftedby 120 degrees, a collimator driving circuit 84, a data collectioncircuit 86 and a bed device 30.

The fixed holder 62 is fixed to the floor of a room in which the scannerdevice 60 is installed. The rotary holder 64 is supported such that itcan rotate around a rotation axis AX with respect to the fixed holder62. The examination subject P is placed on the top board 33 such thatthe body axis thereof is appropriately parallel to the rotation axis AXof the rotary holder 64.

The rotation driving device 66 rotates the rotary holder 64 on therotation axis AX. The rotation driving device 66 includes, for example,a driving means such as a motor, an electronic component for controllingthe driving means, and a transfer means such as a roller which transfersrotary power of a rotation axis of the driving means to the rotaryholder 64. The rotation driving device 66 is controlled by a processingcircuit 50A. For example, the processing circuit 50A can collectprojection data of the examination subject P from a plurality ofdirections by rotating the gamma ray detectors 80 on the rotation axisAX through the rotary holder 64 continuously or by stages.

The gamma ray detectors 80 detect gamma rays radiated from RI(radioactive isotope) such as technetium administered to the examinationsubject P. Detection timing of the gamma ray detectors 80 is controlledby the processing circuit 50A. For example, the gamma ray detectors 80may be scintillator type detectors or semiconductor type detectors. Thiswill be described later.

The collimators 82 control incident angles of gamma rays input to thegamma ray detectors 80. The collimators 82 are formed of a materialthrough which radioactive rays hardly pass, such as lead or tungsten. Aplurality of holes for controlling a direction in which photons move areprovided in the collimators 82. The cross section of these holes mayhave a polygonal shape such as a hexagon, for example.

When the gamma ray detectors 80 are scintillator type detectors, eachgamma ray detector 80 includes, for example, a scintillator which emitsmomentary flash when gamma rays collimated by the collimator 82 areinput, a light guide, a plurality of photoelectron multipliers which aretwo-dimensionally arranged and detect light emitted from thescintillator, and an electronic circuit for a scintillator. Thescintillator is composed of, for example, thallium-activated sodiumiodide NaI(Tl). The electronic circuit for a scintillator generatesincident position information (position information), incident intensityinformation and incident time information of gamma rays within adetection plane formed by the plurality of photoelectron multipliers onthe basis of outputs of the plurality of photoelectron multipliers andoutputs the generated information to the processing circuit 50A of theconsole device 40A whenever a gamma ray input event occurs. The positioninformation may be two-dimensional coordinate information within thedetection plane or information indicating a primary cell to which gammarays have been input from among a plurality of divided regions (primarycells) obtained by virtually dividing the detection plane in advance(for example, dividing the detection plane into 1024×1024 cells).

When the gamma ray detectors 80 are semiconductor type detectors, eachgamma ray detector 80 includes, for example, a plurality ofsemiconductor elements two-dimensionally arranged to detect gamma rayscollimated by the collimator 82, an electronic circuit for asemiconductor, and the like. The semiconductor elements are formed of,for example, CdTe or CdZnTe (CZT). The electronic circuit for asemiconductor generates incident position information, incidentintensity information and incident time information on the basis ofoutputs of the semiconductor elements and outputs the generatedinformation to the processing circuit 50A whenever a gamma ray inputevent occurs. This position information is information indicating asemiconductor element to which gamma rays have been input from among aplurality of semiconductor elements (e.g., 1024×1024 semiconductorelements).

The collimator driving circuit 84 drives the gamma ray detectors 80 andthe collimators 82 in a direction in which they approach or become faraway from the rotation axis AX of the rotary holder 64, for example.

The data collection circuit 86 includes, for example, a printed circuitboard. The data collection circuit 86 executes imaging of theexamination subject P by controlling at least the gamma ray detectors 80according to an instruction from the processing circuit 50A. The datacollection circuit 86 collects detection position information andintensity information of gamma rays detected by the gamma ray detectors80, information representing relative positions of the gamma raydetectors 80 and the examination subject P, and a gamma ray detectiontime by associating them with a gamma ray input event.

The data collection circuit 86 reconstructs two-dimensional imagesacquired as imaging results of the gamma ray detectors 80 and generatesa cross-sectional image (three-dimensional image is also possible)through which a distribution and a movement process ofradiopharmaceuticals are detected.

The top board 33 mounts the examination subject P thereon and controlsmotions of the examination subject P. The bed driving device 32 iscontrolled by the processing circuit 50A to move the top board 33 alongthe rotation axis AX of the rotary holder 64 or in a vertical direction(Y direction in the figure).

[Console Device]

The console device 40A may be a device designed exclusively for thenuclear medical diagnostic apparatus 2 or a device in which a programnecessary for general-purpose personal computers and workstations hasbeen installed. In the former case, some components of the consoledevice 40A may be distributed to the fixed holder 62 and disposedtherein.

FIG. 14 is a diagram showing an example of data stored in a memory 41A.The memory 41A stores, for example, information such as examinationsubject information 41A-1, detected data 41A-2, projection data 41A-3,reconstructed images 41A-4, recommended relative positions 41A-5,trained models 41A-6, and nuclear medical diagnostic images 41A-7 andopinion information 41A-8 generated by the processing circuit 50A. Inaddition, the memory 41A may store programs executed by a hardwareprocessor of the processing circuit 50A.

The processing circuit 50A includes, for example, a preprocessingfunction 52A, a reconstruction processing function 53A, a scan controlfunction 55A, and a learning function 57A. The processing circuit 50Arealizes these functions, for example, by the hardware processorexecuting programs stored in the processing circuit 50A.

The preprocessing function 52A executes preprocessing on the projectiondata 41A-3 acquired by the scan control function 55A which will bedescribed later, for example, on the basis of preprocessing conditioninformation received through the input interface 43 or stored in thememory 41A. Preprocessing includes, for example, uniformity correctionprocessing, rotation center correction processing, preprocessing filterprocessing, processing of converting fan beam projection data intoparallel beam projection data, and the like.

The reconstruction processing function 53A performs reconstructionprocessing on projection data on which preprocessing has been performedby the preprocessing function 52A to generate volume data. The volumedata is stored in the memory 41A as one of nuclear medical diagnosticimages 41A-7. The reconstruction processing function 53A performsreconstruction processing, for example, on the basis of Chang iterativeapproximation method (Iterative Chang). Instead of this, thereconstruction processing function 53A may perform reconstructionprocessing on the basis of maximum likelihood-expectation maximization(ML-EM), ordered subset expectation maximization (OS-EM) and the like.

The scan control function 55A controls some or all of the rotationdriving device 66, the collimator driving circuit 84, the datacollection circuit 86 and the bed device 30 on the basis of a scan planexecution instruction received through the input interface 43 to executescanning and generates the projection data 41A-3 from the datacollection circuit 86.

The learning function 57A stores information about relative positions ofthe gamma ray detectors 80 and the bed device 30 immediately beforeimaging is started and during imaging and information representingrelative positions of the gamma ray detectors 80 and the examinationsubject P as recommended relative positions 41A-5 in addition torecommended positions of the top board 33. The recommended relativepositions 41A-5 include, for example, information about relativepositions of the gamma ray detectors 80 and the bed device 30immediately before imaging is started and information representingrelative positions of the gamma ray detectors 80 and the examinationsubject P (a direction in which the gamma ray detectors 80 approach theexamination subject P, a distance by which the gamma ray detectors 80approach the examination subject P, and the like), or absolute drivingquantities of the gamma ray detectors 80 and the like for realizing thesame. In the description below, information included in the recommendedrelative positions 41A-5 may be simply referred to as “recommendedrelative positions.” Information included in the recommended relativepositions 41A-5 is an example of “relative position information.”

An automatic alignment function 55A-2 selects a trained model 41A-6depending on the examination subject P and sets a recommended relativeposition on the basis of an output result obtained by inputting, to theselected trained model 41A-6, physical characteristics data andinformation about a imaging target portion of examination subjectinformation 41A-1 of the examination subject P output from anexamination subject information acquisition function 55A-1.

The trained model 41A-6 outputs a recommended relative position on thebasis of the physical characteristics data of the examination subject Pand information about an imaging target portion. The trained model 41A-6outputs relative coordinates that should be a feature point of theexamination subject P immediately before scan imaging is started inaddition to the height of the top board 33 as a recommended relativeposition of the bed device 30. Meanwhile, when a headrest, a fixture orthe like is provided at a specific position on the bed device 30 and theposition of the examination subject P on the bed device 30 barelychanges in each examination (an error can be ignored), any portion ofthe top board 33 may be used for alignment of a recommended relativeposition.

FIG. 15 is a diagram showing output processing performed by the trainedmodel 41A-6. The trained model 41A-6 outputs a recommended relativeposition of the recommended relative positions 41A-5 by receivingphysical characteristics data of the examination subject P andinformation about an imaging target portion as parameters.

A manual alignment function 55A-3 receives an input of an operator of amedical image diagnostic apparatus lA with respect to whether to employa recommended relative position set by the automatic alignment function55A-2. When an input of the operator which represents movement to therecommended relative position is received, the manual alignment function55A-3 moves the rotary holder 64, the top board 33 (not shown) and atleast parts of the gamma ray detectors 80 and the collimators 82 to therecommended relative position set by the automatic alignment function55A-2 and then receives an operation of realignment or fine adjustment.In addition, when an input of the operator which represents thatmovement to the recommended relative position is not performed isreceived, the manual alignment function 55A-3 controls operations of therotary holder 64, the top board 33 and at least parts of the gamma raydetectors 80 and the collimators 82 according to the input operation.The manual alignment function 55A-3 reflects results of realignment orfine adjustment in the recommended relative position 41A-5.

FIG. 16 is a diagram showing trained model generation processing of thelearning function 57A. For example, the learning function 57 generates amachine learning model for generating a trained model using physicalcharacteristics data of a certain examination subject P and informationabout a imaging target portion included in the examination subjectinformation 41-1 as learning data and using position information of thegamma ray detectors 80 and position information of the bed device 30such as the top board 33 during imaging included in the recommendedrelative position 41A-5 as teacher data.

FIG. 17 is a flowchart showing an example of a flow of imagingprocessing performed by the nuclear medical diagnostic apparatus 2.

First, the examination subject information acquisition function 55A-1acquires physical characteristics data of the examination subject P andinformation about an imaging target portion stored in the examinationsubject information 41A-1 (step S300). Subsequently, the automaticalignment function 55A-2 determines whether there is a trained model41A-6 suitable for the examination subject P (step S302). A “suitabletrained model 41A-6” may be selected depending on the physicalcharacteristics data of the examination subject P as in the firstembodiment. When it is determined that there is a trained model 41A-6suitable for the examination subject P, the automatic alignment function55A-2 applies the physical characteristics data and information aboutthe imaging target portion which are input parameters to the trainedmodel (step S304) and receives an input of a determination result of anoperator with respect to whether to move the fixed holder 62, the rotaryholder 64 and the bed device 30 to a recommended relative positionoutput as a result of application of the physical characteristics dataand the information about the imaging target portion (step S306).Meanwhile, when there is a plurality of trained models 41A-6 selected instep S302, presence of a plurality of choices may be displayed on thedisplay 42 such that a choice of the operator is received in step S304or a most suitable trained model 41A-6 may be selected in step S304.

When it is determined that an input of the operator which representsmovement of the rotary holder 64 and the like to the recommendedrelative position has been received in step S306, the automaticalignment function 55A-2 moves the rotary holder 64 and the like to therecommended relative position (step S308). When it is determined thatthe input of the operator which represents movement of the rotary holder64 and the like to the recommended relative position has not beenreceived, that is, when it is determined that an input representingalignment through a manual operation has been received in step S306 orwhen the automatic alignment function 55A-2 does not determine thatthere is a suitable trained model 41A-6 in step S302, the manualalignment function 55A-3 receives an input of alignment performed by theoperator and moves the rotary holder 64 and the like (step S310).Subsequently, the learning function 57A stores an alignment resultaccording to step S310 as learning data (step S312).

The manual alignment function 55A-3 receives an input operation offinely adjusting final positions of the rotary holder 64 and the like(step S312) after processing of step S310 or step S308 performed by theautomatic alignment function 55A-2. Subsequently, the scan executionfunction 55A-4 performs imaging (step S314). Hereby, description ofprocessing of this flowchart ends. Meanwhile, the aforementioned stepS312 may be performed after processing of step S308.

According to the console device 40A of the nuclear medical diagnosticapparatus 2 of the above-described second embodiment, it is possible toefficiently perform alignment of the bed device 30 during imaging andthe gamma ray detectors 80 with the position of the examination subjectP by including the automatic alignment function 55A-2 which outputsrecommended relative positions including position information of therotary holder 64, the top board 33 and at least parts of the gamma raydetectors 80 and the collimators 82 by inputting physicalcharacteristics data and information about a imaging target portion ofthe examination subject information 41A-1 to a trained model 41A-6 whichsatisfies conditions of the examination subject P.

Third Embodiment

FIG. 18 is configuration diagram of an X-ray CT apparatus 1B accordingto a third embodiment. Meanwhile, in the description below, componentsand functions the same as those in the first embodiment are denoted bythe same reference signs and detailed description thereof will beomitted. Furthermore, “B” is attached to reference signs of componentsor functions different from those in the first embodiment while havingthe same names as those in the first embodiment. The X-ray CT apparatus1B differs from the X-ray CT apparatus 1 of the first embodiment in thatit does not include the learning function 57.

FIG. 19 is a diagram showing an example of data stored in a memory 41B.The memory 41B stores, for example, information of reference examinationsubject information 41-7 instead of trained models 41-6 of the memory 41of the first embodiment. The reference examination subject information41-7 includes examination subject information 41-1 including anexamination subject different from the examination subject P, andposition information of the top board 33 when the examination subjectdifferent from the examination subject P is imaged and is accumulatedwhenever imaging through the X-ray CT apparatus 1B is performed.

FIG. 20 is a configuration diagram of an automatic alignment function55B-2. The automatic alignment function 55B-2 includes, for example, adetermination function 55B-21 and a processing function 55B-22.

The determination function 55B-21 searches the reference examinationsubject information 41-7 and determines a method for deriving arecommended position of the top board 33 in imaging of the examinationsubject P. The determination function 55B-21 is an example of a“determination unit.” The processing function 55B-22 assists alignmentby setting a recommended position of the top board 33 in imaging of theexamination subject P using a method for deriving a recommended positionof the top board 33 determined by the determination function 55B-21. Theprocessing function 55B-22 is an example of a “processing unit.”

[Recommended Position Derivation Method]

An operator of the X-ray CT apparatus 1B sets a method for deriving arecommended position of the top board 33 before the examination subjectP is imaged.

Methods for deriving a recommended position of the top board 33 include,for example, a method of using reference examination subject information41-7 having imaging conditions most similar to those of the examinationsubject P as an employment condition as it is, method of using referenceexamination subject information 41-7 having a physique most similar tothat of the examination subject P as an employment condition as it is, amethod of using a simple average or a weighted average of a plurality ofpieces of reference examination subject information 41-7 having imagingconditions similar to those of the examination subject P as anemployment condition, a method of using a simple average or a weightedaverage of a plurality of pieces of reference examination subjectinformation 41-7 having a physique similar to that of the examinationsubject P as an employment condition, a method of using a simple averageor a weighted average of a plurality of pieces of reference examinationsubject information 41-7 having imaging conditions and a physiquesimilar to those of the examination subject P as an employmentcondition, and the like.

FIG. 21 is a diagram showing setting conditions for a recommendedposition 41-5. For example, the operator of the X-ray CT apparatus 1Bmay operate the input interface 43 with reference to an image IMdisplayed on the display 42 shown in FIG. 21 to set a method forderiving a recommended position. The image IM includes a condition tableDCT for a method for deriving a recommended position of the top board33, an input format F1 through which a derivation method to be used inthe condition table DCT is set is received, an input format F2 throughwhich processing setting when a recommended position of the top board 33has not been derived through a derivation method designated through theinput format F1 is received, and the like. The input format F2 includesan input format F2-1 through which an alternative method other than aderivation method set through the input format F1 is received and aninput format F2-2 through which processing performed by the automaticalignment function 55B-2 is suspended and setting for promoting manualsetting by the operator of the X-ray CT apparatus 1B is received.

The condition table DCT includes a plurality of methods for deriving arecommended position of the top board 33 which have differentcalculation methods and different number of pieces of referenceexamination subject information 41-7 that are calculation targets. Withrespect to derivation methods included in the condition table DCT,customized input by the operator of the X-ray CT apparatus 1B may bepossible. Further, when a derivation method employing a weighted averageof a plurality of pieces of reference examination subject information41-7 having imaging conditions and the like similar to those of theexamination subject P is set, the operator of the X-ray CT apparatus 1Bmay personally set a degree of weighting performed on a detection valuefrom among the examination subject information 41-1 in the conditiontable DCT. Meanwhile, the determination function 55B-21 may search thereference examination subject information 41-7, select any derivationmethod from the condition table DCT depending on a search result anddetermine the selected derivation method. Imaging conditions includeconditions such as information about a imaging target portion of theexamination subject P (e.g., a chest, a head, or the like), an insertiondirection representing whether the examination subject P will beinserted into the holding device 10 head first (HF) or foot first (FF),whether angiography will be performed, and a posture of the examinationsubject P.

FIG. 22 is a diagram showing processing of the processing function55B-22. The reference examination subject information 41-7 includes aplurality of pieces of physical characteristics data of examinationsubjects and position information (e.g., coordinates and the like) ofthe top board 33 during imaging as shown.

When a derivation method determined by the determination function 55B-21is a derivation method which selects a top board position based onphysical characteristics data of an examination subject most similar tothe physical characteristics data of the examination subject P as arecommended position and employs it, the processing function 55B-22outputs, as a recommended position of the examination subject P, a topboard position of reference examination subject information 41-7 ofanother examination subject having the same imaging conditions as thoseof the examination subject P and a degree of physical characteristicsdata matching that of the examination subject P which is equal to orgreater than a predetermined degree. The processing function 55B-22sets, as a recommended position, for example, position information ofthe top board 33 of an examination subject F having physicalcharacteristics data (sex, height, age, weight and body fat percentage)most similar to that of the examination subject P from among thereference examination subject information 41-7 in the figure.

When the derivation method determined by the determination function55B-21 is a derivation method which derives a top board position basedon physical characteristics data of reference examination subjectinformation 41-7 having the same imaging conditions as those of theexamination subject P as a recommended position, the processing function55B-22 derives an average of top board positions of examination subjectsA to E and sets the average as a recommended position of the examinationsubject P.

When a derivation method determined by the determination function 55B-21is a derivation method employing a bed position based on physicalcharacteristics data of an examination subject having physicalcharacteristics data and imaging conditions most similar to those of theexamination subject P, the processing function 55B-22 outputs, as a bedposition information of the examination subject P, a bed position ofreference examination subject information 41-7 of another examinationsubject having the same imaging conditions as those of the examinationsubject P and a degree of physical characteristics data and imagingcondition matching those of the examination subject P which is equal toor greater than a predetermined degree. The processing function 55B-22sets, as a recommended position, for example, position information ofthe top board 33 of an examination subject A having physicalcharacteristics data (sex, height, age, weight and body fat percentage)and imaging conditions (e.g., contrast radiography and HF/FF) mostsimilar to those of the examination subject P from among the referenceexamination subject information 41-7 in the figure.

When a derivation method determined by the determination function 55B-21is a derivation method which selects and employs a bed position based onphysical characteristics data of one or more other examination subjectsother than the examination subjects, the processing function 55B-22outputs, as bed position information of the examination subject, bedposition information of another examination subject having a derivationcondition closest to that of the examination subject P from among otherexamination subjects having the same imaging conditions as those of theexamination subject P and degrees of physical characteristics datamatching that of the examination subject P which is equal to or greaterthan a predetermined degree.

When the derivation condition is defined, for example, as a conditionthat an error between physical characteristics data of the examinationsubject P and physical characteristics data of another examinationsubject included in the reference examination subject information 41-7is less than a predetermined threshold value, the determination function55B-21 extracts reference examination subject information 41-7 having anerror less than the predetermined threshold value and then derives a bedposition from the extraction result.

For example, when the operator of the X-ray CT apparatus 1B sets aheight error of less than 5[%] as a predetermined threshold value, thedetermination function 55B-21 determines that reference examinationsubject information 41-7 of an examination subject B will not beemployed because the height of the examination subject B and the heightof the examination subject P has an error therebetween which is equal toor greater than the predetermined threshold value. In this case, theprocessing function 55B-22 outputs, for example, a simple average (or aweighted average) of bed position information of examination subject Aand examination subjects C and E having the same imaging conditions asthose of the examination subject P as bed position information of theexamination subject P.

In addition, when the operator of the X-ray CT apparatus 1B sets an ageerror of less than 5 [years] as a predetermined threshold value, thedetermination function 55B-21 determines that reference examinationsubject information 41-7 of the examination subject C will not beemployed because the age of the examination subject C and the age of theexamination subject P has an error therebetween which is equal to orgreater than the predetermined threshold value. In this case, theprocessing function 55B-22 outputs, for example, a simple average (or aweighted average) of bed position information of the examinationsubjects A and B and examination subjects D and E having the sameimaging conditions as those of the examination subject P as bed positioninformation of the examination subject P.

Furthermore, when the operator of the X-ray CT apparatus 1B sets aweight error of less than 3 [kg] as a predetermined threshold value, thedetermination function 55B-21 determines that reference examinationsubject information 41-7 of the examination subject D will not beemployed because the weight of the examination subject D and the weightof the examination subject P has an error therebetween which is equal toor greater than the predetermined threshold value. In this case, theprocessing function 55B-22 outputs, for example, a simple average (or aweighted average) of bed position information of the examinationsubjects A to C and the examination subject E having the same imagingconditions as those of the examination subject P as bed positioninformation of the examination subject P.

Further, when the operator of the X-ray CT apparatus 1B sets a body fatpercentage error of less than 3 [%] as a predetermined threshold value,the determination function 55B-21 determines that reference examinationsubject information 41-7 of the examination subject E will not beemployed because the body fat percentage of the examination subject Eand the body fat percentage of the examination subject P has an errortherebetween which is equal to or greater than the predeterminedthreshold value. In this case, the processing function 55B-22 outputs,for example, a simple average (or a weighted average) of bed positioninformation of the examination subjects A to D having the same imagingconditions as those of the examination subject P as bed positioninformation of the examination subject P.

In addition, the operator of the X-ray CT apparatus 1B may setpredetermined threshold values for a plurality of pieces of physicalcharacteristics data. For example, when the operator of the X-ray CTapparatus 1B sets a height error of less than 5[%] and an age error ofless than 5 [years] as predetermined threshold values, it is determinedthat reference examination subject information 41-7 of the examinationsubjects B and C will not be employed. In this case, the processingfunction 55B-22 outputs, for example, a simple average (or a weightedaverage) of bed position information of the examination subjects D and Ehaving the same imaging conditions as those of the examination subject Pas bed position information of the examination subject P.

The fact that an error between the physical characteristics data of theexamination subject P and physical characteristics data of anotherexamination subject included in reference examination subjectinformation 41-7 is less than a predetermined value may be defined by aspecific numerical value, defined using the look-up table of FIG. 7 (arange in which the same choices are set, a range in which neighboringchoices are set, and the like in the look-up table), or defined by astatistical reliability section or the like.

When the operator of the X-ray CT apparatus 1B sets the same imagingconditions as a predetermined threshold value, the determinationfunction 55B-21 determines that reference examination subjectinformation 41-7 of examination subjects F and G will not be employedbecause the examination subject F with contrast radiography and theexamination subject G with an insertion direction of FF have differentimaging conditions.

In addition, when the examination subject information 41-1 includes atest purpose and precaution information of the examination subject P,the determination function 55B-21 may extract reference examinationsubject information 41-7 on the basis of the test purpose and theprecaution information of the examination subject P. For example, whenthe examination subject information 41-1 includes precaution informationof “presence of abdominal inflation,” the determination function 55B-21preferentially extracts reference examination subject information 41-7including the precaution information of “presence of abdominalinflation.”

FIG. 23 is a flowchart showing an example of a flow of imagingprocessing performed by the X-ray CT apparatus 1B.

First, the examination subject information acquisition function 55-1acquires physical characteristics data of the examination subject P andinformation about an imaging target portion recorded in the examinationsubject information 41-1 (step S400). Then, the automatic alignmentfunction 55B-2 derives a recommended position (step S402). Processing ofstep S402 will be described later. Subsequently, the automatic alignmentfunction 55B-2 receives an input of a determination result from anoperator with respect to whether to employ the recommended position(step S404).

The automatic alignment function 55B-2 moves the top board 33 to therecommended position when it is determined that an input of the operatorwhich represents movement of the top board 33 to the recommendedposition has been received (step S406). When it is determined that aninput of the operator which represents movement of the top board 33 tothe recommended position has not been received, the manual alignmentfunction 55-3 receives an input of alignment from the operator and movesthe top board 33 (step S408).

The manual alignment function 55-3 receives an input operation of finelyadjusting a final position of the top board 33 after processing of stepS406 or step S408 (step S410). Then, the scan execution function 55-4performs imaging (step S412). Hereby, description of processing of thisflowchart ends.

FIG. 24 is a flowchart showing an example of a flow of recommendedposition derivation processing performed by the automatic alignmentfunction 55B-2. The flowchart shown in FIG. 24 corresponds to step S402of FIG. 23.

The determination function 55B-21 searches reference examination subjectinformation 41-7 which satisfies a derivation condition set in advance(step S500). The derivation condition in step S500 is, for example, aderivation method designated in the input format F1 of FIG. 21. Thedetermination function 55B-21 determines whether a search result forreference examination subject information 41-7 which satisfies thederivation condition has been acquired (step S502). When it isdetermined that a search result for reference examination subjectinformation 41-7 which satisfies the derivation condition has beenacquired, the processing function 55B-22 derives a recommended positionof the top board 33 on the basis of the search result for the referenceexamination subject information 41-7 which satisfies the derivationcondition (step S504). When it is determined that a search result forreference examination subject information 41-7 which satisfies thederivation condition has not been acquired, the processing function55B-22 derives a recommended position of the top board 33 on the basisof a search result for reference examination subject information 41-7which satisfies an alternative condition (step S506). The alternativecondition in step S506 is, for example, a derivation method designatedin the input format F2 of FIG. 21. Hereby, description of processing ofthis flowchart ends.

Meanwhile, the flowchart shown in FIG. 24 describes a flow of processingwhen another derivation condition is selected as an alternativecondition. When the input format F2-2 is selected as an alternativecondition, processing of suspending derivation of a recommended positionof the top board 33 is performed in step S506.

According to the X-ray CT apparatus 1B of the above-described thirdembodiment, it is possible to efficiently perform operations such asalignment of the examination subject P before imaging by including theexamination subject information acquisition function 55-1 which acquiresexamination subject information 41-1 of the examination subject P, thedetermination function 55B-21 which determines a method for deriving arecommended position information of the top board 33 on the basis ofexamination subject information 41-1 including physical characteristicsdata of the examination subject P and information about a imaging targetportion, and the processing function 55B-22 which outputs a recommendedposition of the top board 33 with respect to the examination subject Pthrough the derivation method determined by the determination function55B-21.

The above-described embodiments can be represented as follows.

A medical image diagnostic apparatus including:

a storage which stores a program; and

a processor,

wherein the processor, by executing the program,

acquires physical characteristics data of an examination subject andinformation about an imaging target portion, and

outputs bed position information by inputting the physicalcharacteristics data and the information about the imaging targetportion to a trained model which is configured to output the bedposition information on the basis of the physical characteristics dataand the information about the imaging target portion.

According to at least one embodiment described above, it is possible toefficiently perform operations such as alignment of a bed device beforeimaging by including an acquisition unit (55-1, 55A-1) which isconfigured to acquire examination subject information (41-1) includingphysical characteristics data of an examination subject and informationabout a imaging target portion, and a processing unit (55-2, 55A-2)which is configured to output a recommended position of the bed device(30) by inputting the physical characteristics data of the examinationsubject and the information about the imaging target portion to atrained model (41-6, 41A-6) which satisfies conditions of theexamination subject P.

While preferred embodiments of the invention have been described andillustrated above, it should be understood that these are exemplary ofthe invention and are not to be considered as limiting. Additions,omissions, substitutions, and other modifications can be made withoutdeparting from the spirit or scope of the present invention.Accordingly, the invention is not to be considered as being limited bythe foregoing description, and is only limited by the scope of theappended claims.

1. A medical image diagnostic apparatus comprising a processing circuitry configured to: acquire physical characteristics data of an examination subject and information about an imaging target portion; and output bed position information about the examination subject according to the acquired physical characteristics data and the information about the imaging target portion by inputting the physical characteristics data and the information about the imaging target portion to a trained model which is configured to output bed position information on the basis of the physical characteristics data and the information about the imaging target portion.
 2. The medical image diagnostic apparatus according to claim 1, wherein the bed position information includes a height of a top board of a bed with respect to a reference position.
 3. The medical image diagnostic apparatus according to claim 1, wherein the processing circuitry is further configured to select one or more trained models from a plurality of trained models on the basis of precaution information of the examination subject and output the bed position information by inputting the physical characteristics data and the information about the imaging target portion to the selected one or more trained models.
 4. The medical image diagnostic apparatus according to claim 3, wherein the precaution information includes information which becomes an artifact factor imaged in an image captured by the apparatus.
 5. The medical image diagnostic apparatus according to claim 4, wherein the information which becomes the artifact factor is information on inflation with respect to an imaging target portion.
 6. The medical image diagnostic apparatus according to claim 1, wherein the processing circuitry is further configured to generate the trained model by performing machine learning using the physical characteristics data and the information about the imaging target portion as learning data and using the bed position information set for the same examination subject as teacher data.
 7. A medical image diagnosis method, using a computer, comprising: acquiring physical characteristics data of an examination subject and information about an imaging target portion; and outputting bed position information about the examination subject according to the acquired physical characteristics data and the information about the imaging target portion by inputting the acquired physical characteristics data and the information about the imaging target portion to a trained model which is configured to output bed position information on the basis of physical characteristics data and information about a imaging target portion.
 8. A computer-readable non-transitory storage medium storing a program causing a computer to: acquire physical characteristics data of an examination subject and information about an imaging target portion; and output bed position information about the examination subject according to the acquired physical characteristics data and the information about the imaging target portion by inputting the acquired physical characteristics data and the information about the imaging target portion to a trained model which is configured to output bed position information on the basis of physical characteristics data and information about a imaging target portion.
 9. A medical image diagnostic apparatus comprising a processing circuitry configured to: acquire physical characteristics data of an examination subject and information about an imaging target portion; and output relative position information about the examination subject according to the acquired physical characteristics data and the information about the imaging target portion by inputting the physical characteristics data and the information about the imaging target portion to a trained model which is configured to output relative position information of the medical image diagnostic apparatus and the examination subject during scanning on the basis of the physical characteristics data and the information about the imaging target portion.
 10. The medical image diagnostic apparatus according to claim 9, wherein the relative position information includes at least position information of the examination subject and a detector of the medical image diagnostic apparatus.
 11. A medical image diagnostic apparatus comprising a processing circuitry configured to: acquire physical characteristics data of an examination subject and information about an imaging target portion; determine a method for deriving a recommended position information of a bed position on the basis of the physical characteristics data and the information about the imaging target portion; and output bed position information about the examination subject according to the acquired physical characteristics data and the information about the imaging target portion through a determined method for deriving.
 12. The medical image diagnostic apparatus according to claim 11, wherein the determined method for deriving is a derivation method employing bed position information based on physical characteristics data of an examination subject other than the examination subject, and wherein outputted bed position information of the examination subject is bed position information of the other examination subject having the same imaging conditions as those of the examination subject and a degree of physical characteristics data matching that of the examination subject which is equal to or greater than a predetermined degree.
 13. The medical image diagnostic apparatus according to claim 11, wherein the determined method for deriving is a derivation method which derives bed position information based on physical characteristics data of one or more examination subjects other than the examination subject which have the same imaging conditions as those of the examination subject, and wherein the processing circuitry is further configured to derive an average of bed position information associated with the other examination subjects and uses the average as bed position information of the examination subject.
 14. The medical image diagnostic apparatus according to claim 11, wherein the determined method for deriving is a derivation method which is configured to select bed position information based on physical characteristics data of one or more examination subjects other than the examination subject, and wherein the processing circuitry is further configured to output, as bed position information of the examination subject, bed position information of the other examination subject having an employment condition closest to that of the examination subject from among the other examination subjects having the same imaging conditions as those of the examination subject and degrees of physical characteristics data matching that of the examination subject which are equal to or greater than a predetermined degree.
 15. The medical image diagnostic apparatus according to claim 14, wherein the employment condition is a condition that an error between the physical characteristics data of the other examination subject and the physical characteristics data of the examination subject is less than a predetermined value. 